Move a model from development to production.
Track data, code, model, and evaluation and be able to recover.
Choose two operational practices.
Version data, code, model, settings, and results, and monitor quality, drift, and cost with retraining and rollback criteria.
Detailed explanation
Changes can be reproduced and investigated.
Changes can be reproduced and investigated.
Production degradation can be handled.
Production degradation can be handled.
Data and service conditions change.
Data and service conditions change.
Changes cannot be reproduced or reviewed.
Changes cannot be reproduced or reviewed.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.3の機械学習ライフサイクル、MLOps、監視を確認する。Expected result
モデル開発と本番運用を連続したライフサイクルとして説明できる。Key points
- Versioning
- Monitoring
- Rollback
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.